Project Summary/Abstract Yoga can reduce chronic low back pain (CLBP) and may help decrease reliance on prescription pain medication. CLBP is the leading cause of morbidity in the world and back pain-related disability rates continue to climb in the U.S. despite increasing healthcare utilization and cost. Additionally, CLBP disproportionately affects low-income minority populations who have less access to non-pharmacologic treatment options. Identifying and integrating safe cost-effective non-pharmacologic CLBP treatment options throughout the mainstream healthcare system is paramount. Clinical prediction rules have provided guidance for the management of back pain, helping clinicians to determine which treatment (e.g., spinal manipulation, therapeutic exercise) will work best for which patient. Despite a rapid increase in Americans practicing yoga and a number of clinical trials showing effectiveness, a clinical prediction rule to identify which CLBP patients will most likely benefit from yoga does not yet exist. The NIH Task Force on Research Standards for CLBP recently recommended future research should report a responder analysis,? i.e., the proportion of participants achieving certain thresholds of improvement (e.g., a 30% improvement from baseline in back-related function measured by Roland Morris Disability Questionnaire [RMDQ] scores). Through identifying baseline characteristics of ?responders? to a given CLBP treatment, clinical prediction rules can aid clinician-patient shared decision-making across a wider range of non-pharmacologic therapies. The applicant, Eric Roseen, a doctor of chiropractic, will develop a CLBP clinical prediction rule for yoga through secondary data analyses using a dataset collected by his sponsor and primary mentor, Robert Saper, MD, MPH of Boston University. Dr. Saper is PI of Back to Health (R01-AT005956), an NCCIH-funded RCT (N=320) comparing the effectiveness of yoga, physical therapy, and education for treatment of non-specific CLBP in a predominantly low-income minority population. The proposed study will use the Back to Health dataset to identify baseline characteristics of treatment ?responders? compared to ?non-responders? in a total of 127 yoga participants and develop a yoga CLBP clinical prediction rule (Aim 1). Subsequently, the CLBP yoga clinical prediction rule will be validated in two additional datasets (Aim 2) collected by Saper et al: (1) Yoga Dosing Study, a similar population of 95 low-income minority participants with CLBP randomized to 12 weeks of once- or twice-weekly yoga classes. (2) Veterans Back to Health, an RCT including one arm of 60 predominantly white male Veterans with CLBP receiving 12 weekly yoga classes. We hypothesize that participants with high BMI, anxiety, depression, and no opioid use at baseline will be more likely to be a ?responder,? defined as having a 30% improvement in back-related function, using RMDQ. Furthermore when tested in additional data sets, participants positive on the preliminary clinical prediction rule will be more likely to be treatment ?responders.?